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Affective abstraction refers to how people conceptualize affective states in terms of category-level re- presentations that generalize across speci!c situations (e.g., “fear” as evoked by heights, predators, and haunted houses). Here, we develop a novel task for assessing affective abstraction and test its relations with trait alexithymia, depression, and autism spectrum quotient. In a preregistered online study, participants completed a set of tasks in which they matched a cue image with one of two probe images based on similarity of affective experience. In a discrete emotion version of the task, the cue and target probe matched on a discrete emotion category while controlling for valence. In a valence version of the task, the cue and target probe matched on valence (i.e., pleasantness or unpleasantness). We further varied the degree of abstraction such that some judgments crossed semantic categories (e.g., a house cue with animal probes). Accuracy, as indexed by the proportion of choices that accorded with norms, predicted trait measures of alexithymia, depression, and autism quotient with medium effect sizes. We conducted an integrative data analysis by including data from three other (nonpreregistered) samples (N = 435) and found substantial moderation by sampling population (Amazon Mechanical Turk, college students) and partial moderation by gender identity. Additional constraints on generalization include that our sample included predominantly White American adults between the ages of 23 and 64. These results provide preliminary support for the notion that affective abstraction may re"ect a transdiagnostic psychological process of broad relevance to individual differences in affective processing. 

Abstract Traditionally, lust and pride have been considered pleasurable, yet sinful in the West. Conversely, guilt is often considered aversive, yet valuable. These emotions illustrate how evaluations about specific emotions and beliefs about their hedonic properties may often diverge. Evaluations about specific emotions may shape important aspects of emotional life (e.g. in emotion regulation, emotion experience and acquisition of emotion concepts). Yet these evaluations are often understudied in affective neuroscience. Prior work in emotion regulation, affective experience, evaluation/attitudes and decision-making point to anterior prefrontal areas as candidates for supporting evaluative emotion knowledge. Thus, we examined the brain areas associated with evaluative and hedonic emotion knowledge, with a focus on the anterior prefrontal cortex. Participants (N = 25) made evaluative and hedonic ratings about emotion knowledge during functional magnetic resonance imaging (fMRI). We found that greater activity in the medial prefrontal cortex (mPFC), ventromedial PFC (vmPFC) and precuneus was associated with an evaluative (vs hedonic) focus on emotion knowledge. Our results suggest that the mPFC and vmPFC, in particular, may play a role in evaluating discrete emotions. 

Abstract Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA’s utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so.